'use strict'; /** * https://github.com/gre/bezier-easing * BezierEasing - use bezier curve for transition easing function * by Gaëtan Renaudeau 2014 - 2015 – MIT License */ // These values are established by empiricism with tests (tradeoff: performance VS precision) const NEWTON_ITERATIONS = 4; const NEWTON_MIN_SLOPE = 0.001; const SUBDIVISION_PRECISION = 0.0000001; const SUBDIVISION_MAX_ITERATIONS = 10; const kSplineTableSize = 11; const kSampleStepSize = 1.0 / (kSplineTableSize - 1.0); function A(aA1: number, aA2: number): number { 'worklet'; return 1.0 - 3.0 * aA2 + 3.0 * aA1; } function B(aA1: number, aA2: number): number { 'worklet'; return 3.0 * aA2 - 6.0 * aA1; } function C(aA1: number) { 'worklet'; return 3.0 * aA1; } // Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2. function calcBezier(aT: number, aA1: number, aA2: number): number { 'worklet'; return ((A(aA1, aA2) * aT + B(aA1, aA2)) * aT + C(aA1)) * aT; } // Returns dx/dt given t, x1, and x2, or dy/dt given t, y1, and y2. function getSlope(aT: number, aA1: number, aA2: number): number { 'worklet'; return 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1); } function binarySubdivide( aX: number, aA: number, aB: number, mX1: number, mX2: number ): number { 'worklet'; let currentX; let currentT; let i = 0; do { currentT = aA + (aB - aA) / 2.0; currentX = calcBezier(currentT, mX1, mX2) - aX; if (currentX > 0.0) { aB = currentT; } else { aA = currentT; } } while ( Math.abs(currentX) > SUBDIVISION_PRECISION && ++i < SUBDIVISION_MAX_ITERATIONS ); return currentT; } function newtonRaphsonIterate( aX: number, aGuessT: number, mX1: number, mX2: number ): number { 'worklet'; for (let i = 0; i < NEWTON_ITERATIONS; ++i) { const currentSlope = getSlope(aGuessT, mX1, mX2); if (currentSlope === 0.0) { return aGuessT; } const currentX = calcBezier(aGuessT, mX1, mX2) - aX; aGuessT -= currentX / currentSlope; } return aGuessT; } export function Bezier( mX1: number, mY1: number, mX2: number, mY2: number ): (x: number) => number { 'worklet'; function LinearEasing(x: number): number { 'worklet'; return x; } if (!(mX1 >= 0 && mX1 <= 1 && mX2 >= 0 && mX2 <= 1)) { throw new Error('[Reanimated] Bezier x values must be in [0, 1] range.'); } if (mX1 === mY1 && mX2 === mY2) { return LinearEasing; } // FIXME: Float32Array is not available in Hermes right now // // var float32ArraySupported = typeof Float32Array === 'function'; // const sampleValues = float32ArraySupported // ? new Float32Array(kSplineTableSize) // : new Array(kSplineTableSize); // Precompute samples table const sampleValues = new Array(kSplineTableSize); for (let i = 0; i < kSplineTableSize; ++i) { sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2); } function getTForX(aX: number): number { 'worklet'; let intervalStart = 0.0; let currentSample = 1; const lastSample = kSplineTableSize - 1; for ( ; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample ) { intervalStart += kSampleStepSize; } --currentSample; // Interpolate to provide an initial guess for t const dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]); const guessForT = intervalStart + dist * kSampleStepSize; const initialSlope = getSlope(guessForT, mX1, mX2); if (initialSlope >= NEWTON_MIN_SLOPE) { return newtonRaphsonIterate(aX, guessForT, mX1, mX2); } else if (initialSlope === 0.0) { return guessForT; } else { return binarySubdivide( aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2 ); } } return function BezierEasing(x) { 'worklet'; if (mX1 === mY1 && mX2 === mY2) { return x; // linear } // Because JavaScript number are imprecise, we should guarantee the extremes are right. if (x === 0) { return 0; } if (x === 1) { return 1; } return calcBezier(getTForX(x), mY1, mY2); }; }